Requirements
* Have hands‑on experience training large language models, or deep expertise with JAX, TPU, PyTorch, or large‑scale distributed systems
* Genuinely enjoy both research and engineering work—you'd describe your ideal split as roughly 50/50 rather than heavily weighted toward one or the other
* Are excited about being on‑call for production systems, working long days during launches, and solving hard problems under pressure
* Thrive when working on whatever is most impactful, even if that changes day‑to‑day based on what the production model needs
* Excel at debugging complex, ambiguous problems across multiple layers of the stack
* Communicate clearly and collaborate effectively, especially when coordinating across time zones or during high‑stress incidents
* Are passionate about the work itself and want to refine your craft as a research engineer
* Care about the societal impacts of AI and responsible scaling
* (Desirable) Previous experience training LLM’s or working extensively with JAX/TPU, PyTorch, or other ML frameworks at scale
* (Desirable) Contributed to open‑source LLM frameworks (e.g., open_lm, llm‑foundry, mesh‑transformer‑jax)
* (Desirable) Published research on model training, scaling laws, or ML systems
* (Desirable) Experience with production ML systems, observability tools, or evaluation infrastructure
* (Desirable) Background as a systems engineer, quant, or in other roles requiring both technical depth and operational excellence
* We require at least a Bachelor's degree in a related field or equivalent experience
* We encourage you to apply even if you do not believe you meet every single qualification
What the job involves
* Anthropic's ML Performance and Scaling team trains our production pretrained models, work that directly shapes the company's future and our mission to build safe, beneficial AI systems
* As a Research Engineer on this team, you'll ensure our frontier models train reliably, efficiently, and at scale
* This is demanding, high‑impact work that requires both deep technical expertise and a genuine passion for the craft of large‑scale ML systems
* This role lives at the boundary between research and engineering
* You'll work across our entire production training stack: performance optimization, hardware debugging, experimental design, and launch coordination
* During launches, the team works in tight lockstep, responding to production issues that can't wait for tomorrow
* Own critical aspects of our production pretraining pipeline, including model operations, performance optimization, observability, and reliability
* Debug and resolve complex issues across the full stack—from hardware errors and networking to training dynamics and evaluation infrastructure
* Design and run experiments to improve training efficiency, reduce step time, increase uptime, and enhance model performance
* Respond to on‑call incidents during model launches, diagnosing problems quickly and coordinating solutions across teams
* Build and maintain production logging, monitoring dashboards, and evaluation infrastructure
* Add new capabilities to the training codebase, such as long context support or novel architectures
* Collaborate closely with teammates across SF and London, as well as with Tokens, Architectures, and Systems teams
* Contribute to the team's institutional knowledge by documenting systems, debugging approaches, and lessons learned
* This is not a typical research engineering role
* The work is highly operational—you'll be deeply involved in keeping our production models training smoothly, which means being responsive to incidents, flexible about priorities, and comfortable with uncertainty
* During launches, the team often works extended hours and may need to respond to issues on evenings and weekends
* However, this operational intensity comes with extraordinary learning opportunities
* You'll gain hands‑on experience with some of the largest, most sophisticated training runs in the industry
* You'll work alongside world‑class researchers and engineers, and the institutional knowledge you build will compound in ways that can't be easily transferred
* For people who thrive on this type of work, it's uniquely rewarding. We're building a close‑knit team of people who genuinely care about doing excellent work together
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